Icertis. has been granted a patent for a machine learning engine that manages contract documents. The engine selects training and validation documents, evaluates attributes, and generates confirmed models to predict attribute values, enhancing prediction accuracy and resource efficiency through geolocation and semantic evaluations. GlobalData’s report on Icertis gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Icertis, Speech recognition techniques was a key innovation area identified from patents. Icertis's grant share as of July 2024 was 62%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning engine for managing contract document attributes
The granted patent US12020130B2 outlines a method and system for managing contract documents over a network, utilizing one or more processors to execute specific actions. The method involves instantiating an engine that determines training and validation contract documents from a larger set. It employs both semantic and textual evaluators to identify attributes related to localization features, such as time zones, languages, and currencies. Additionally, the system incorporates geolocation devices or protocols to gather geolocation information, which is then used to validate categories of contract clauses or document types relevant to the physical location. The process includes selecting candidate models based on training accuracy scores, evaluating and ranking these models, and generating confirmed models to predict attribute values of selected contract documents, thereby enhancing the efficiency of computing resources and the accuracy of predictions.
Further claims of the patent detail additional functionalities, such as training multiple models to predict attribute values, identifying candidate attributes within the contract documents, and determining attribute values based on these evaluations. The system also allows for the identification of outlier attributes and the prediction of unknown attribute values for working documents using high-ranked candidate models. The architecture includes a network computer with memory and processors that execute the outlined instructions, ensuring a comprehensive approach to contract document management that leverages machine learning and geolocation technologies. This innovative method aims to streamline the management of contract documents while improving accuracy and resource efficiency.
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